It has been reported that the fast align approach is more than 10 times faster than baseline GIZA++, with comparable results in end-to-end French-, Chinese-, and Arabic-to-English translation experiments.

the simplicity of the IBM model 2 also leads to a limitation.


The translations from English improved (equal to GIZA++) at the second iteration.

translations to English improved more slowly.


(Bach et al., 2011)) to update the cost of the search graph hypotheses.


In the adaptation task, the discriminative training of the CTM gives a large improvement of 0.9 BLEU score over the CTM only trained with NCE and 1.9 over the baseline system.

for the training scenario, these gains are reduced respectively to 0.4 and 1.2 BLEU points.


Suggestions from a machine translation system can increase the speed and quality of professional human translators (Guerberof, 2009;Plitt and Masselot, 2010;Green et al., 2013a, inter alia).

querying a single fixed model for all different documents fails to incorporate contextual information that can potentially improve suggestion quality.


Other popular metrics like METEOR (Denkowski and Lavie, 2014) and TERp (Snover et al., 2008) also use external resources like WordNet and paraphrase databases.

system-level correlation with human judgements for these metrics remains below 0.90 Pearson correlation coefficient (as per WMT-14 results, BLEU-0.888, NIST-0.867, METEOR-0.829, TER-0.826, WER-0.821).


2) of any aligned phrase pairs (s, t,â), assuming it is composed of embeddable words.

we found the supervised word translation scores q to be too sharp, sometimes assigning all probability mass to a single target word.


As before, we added our supervised lexical weights as new features in the phrase table.

instead of fixing β = 0.95 as above, we searched for β ∈ {0.9, 0.8, 0.7, 0.6} in Eq.


Note that LSA on just the English data performs on par with all of the other methods presented; we have not found a way to improve performance on this monolingual task from using multilingual data.

1 it is also important to note that our multilingual methods do not hurt performance on these monolingual tasks, either-we get the benefits described in our other evaluations without losing performance on English-only tasks.


This technology has advanced to the point where it is becoming capable enough to be useful for many applications.

this approach may be unsuitable for simultaneous interpretation where the machine translation system is required to provide translations within a reasonably short space of time after words have been spoken.


The default rule scoring procedure for string-to-tree rules implemented in Moses uses the same normalization as we do.

williams and Koehn (2012) propose to normalize string-to-tree rules over the source rhs only.


Such an approach has already shown to be useful for several NLP tasks (Volkova et al., 2013;Hovy, 2015).

before embarking on this challenging task, we explore if the above concerns are founded by addressing the research question: does MT has an impact on the classification of demographic and personality traits?


5 The WIT3 data seems to also include data in the fr-en direction.

in practice, TED hosts only talks in English and all foreign to English corpora were collected from the translated versions of the site.


It has been shown that standard approaches to gender classification on English texts can be sub-optimal for non-English language data (Ciot et al., 2013).

state-of-the-art classification results are not our focus; rather, our intention is to understand the impact of translation on classification of socio-demographic and personality traits.


We are interested in understanding the impact which the consideration of author traits might have on automatic translation, in order to preserve projection of those traits in a target language.

it is first necessary to understand the inverse: the effect of current translation approaches on the computational recognition of these traits.


This idea allows processing corpus aligned at sentence-level rather than word-level.

it does not leverage the abundance of existing mono-lingual corpora .


For CONTINGENCY, Condition is not classified as continuous or discontinuous, and it is highly marked, thus driving the overall score high.

if Condition is removed, then CONTIN-GENCY will be the least marked relation at the level 1.


Results show low to moderate improvement over the baseline for gender classification, with the combination of semantic features and unigrams being the best performing feature set.

our classifiers performed poorly in the age prediction task, with accuracies below the majority class baseline.


For instance, regardless of their gender, older deceivers use references to anxiety, money, and motion.

younger deceivers language includes anger, negate, and death words.


Through several experiments, we showed that this data can be used to build deception classifiers for short open domain text.

the classifiers do not per-form very well while trying to predict gender and age.


Another proposed model of phonotactics is the Minimal Generalization Learner, or MGL (Albright, 2009); Linzen and Gallagher (2014) showed that MGL can simulate relevant human behavioral data in some circumstances.

with PAIM and MaxEnt, which converge to the empirical distribution given sufficient data, MGL reserves a fixed amount of probability mass to unseen events.


Such a comprehensive type system makes it possible to define various kinds of functions and to perform type-compatibility checking.

most previous semantic languages have at most 100+ types at the grammar level.


instances with more than one role), to allow generalization of flat structures.

our trees are unlike syntactic constituent trees in that they do not have labeled nonterminal nodes, so we have no natural choice of an intermediate ("bar") label.


For example, in the sentence "The big fish ate the little fish," ini- tially both English 'fish' are aligned to both AMR fish.

based on the context of 'big' and 'little' the spurious links are removed.


We would like to tune our feature weights to maximize Smatch directly.

a very convenient alternative is to compare the AMRese yields of candidate AMR parses to those of reference AMRese strings, using a BLEU objective and forest-based MIRA (Chiang et al., 2009).


(2015) also uses a two-pass approach; dependency parses are modified by a tree-walking algorithm that adds edge labels and restructures to resolve discrepancies between dependency standards and AMR's specification.

to these works, we use a single-pass approach and re-use existing machine translation architecture, adapting to the AMR parsing task by modifying training data and adding lightweight AMRspecific features.


At a particular training size, a linear regression model has estimated that improving alignment quality by 1 edit distance toward the gold standard alignments leads to an 3.80-4.70% increase in G2P transcription accuracy.

we have also found that the importance of good alignments on G2P accuracy appears to dimish as data set size increases, possibly because the translation modules can accomodate more 'noisy' data in this scenario.


As in existing statistical IM engines, parameters are estimated from a corpus whose sentences are segmented into words annotated with their pronunciations as follows: where F (•) denotes the frequency of a pair sequence in the corpus.

to IM engines based on a word n-gram model, ours does not require an additional model describing relationships between words and pronunciations, and thus it is much simpler and more practical.


Example of Log-mconv (3 annotations) As this example shows, Log-mconv contains short entries (fragmentation) like Log-as-is.

we expect that the annotated tweets do not include mistaken boundaries or conversions that were discarded.


This practice means that these methods can typically yield high performance in certain specialized domains, but they lack generalizability.

with these methods, we propose to leverage bilingual unlabeled data, i.e., a Chinese-English corpus with sentence alignment.


The results presented in Table 4 indicate that SLBD demonstrates much stronger performance, primarily because these other methods were developed with a focus on SMT, which causes them to preferentially decrease the perplexity of the subsequent SMT steps rather than producing a highly accurate segmentation.

to these methods, the SLBD method exhibits greater generalizability.


With HPBSMT, a restricted form of an SCFG, i.e., Hiero grammar, is usually used and is especially suited for linguistically divergent language pairs, such as Japanese and English.

a rule table, i.e., a synchronous grammar, may be composed of spuriously many rules with potential errors especially when it was automatically acquired from a parallel corpus.


Rule arithmetic method (Cmejrek and Zhou, 2010) can generate SCFG rules by combining other rule pairs through an insideoutside algorithm.

those previous attempts were restricted in that the rules and phrases were induced by heuristic combination.


If r k already exists in a table, we draw r k with probability where c k is the number of customers of r k , n r is the number of all customers and φ r k is a number of r k 's tables.

if r k is a new rule, we draw r k with probability where |φ r | is the number of tables in the model.


As a result, larger phrase pairs were forced to be constructed from those minimal rules.

our back-off model could directly express phrase pairs of multiple granularities.


As a result, they have to resort to realigning.

the consistency constraint used in most translation rule extraction algorithms tolerate wrong links within consistent phrase pairs.


In Figure 1, (f 3 1 , e 3 1 ) is consistent with the alignment because all words in "oumeng he eluosi" are aligned with all words in "EU and Russia".

in Figure 1(b), "huiwu shounao" and "hold summit" are not consistent with the alignment because "hold" is also aligned to a word "juxing" outside.


In contrast, in Figure 1(b), "huiwu shounao" and "hold summit" are not consistent with the alignment because "hold" is also aligned to a word "juxing" outside.

alignment consistency only defines a loose relationship between alignment and translation.


A natural way is to include consistency in the optimization objective as a regularization term.

as consistency is only defined at the phrase level (see Definition 4), we need a sentence-level measure to reflect how well an alignment conforms to the consistency constraint.


Another alternative is reachability (Liang et al., 2006a;Yu et al., 2013) that indicates whether there exists a full derivation to recover the training data.

calculating reachability faces a major problem: a large portion of training data cannot be fully recovered due to noisy alignments and the distortion limit (Yu et al., 2013).


(2013) indicate that using forced decoding to select reachable sentences with an unlimited distortion limit runs in O(2 n n 3 ) time.

calculating coverage is much easier and more efficient by ignoring the dependency between phrases but still retains the spirit of measuring recovery.


Efforts using source-side associations mainly focus on the exploitation of either sentence-level context (Chan et al., 2007;Carpuat and Wu, 2007;Hasan et al., 2008;Mauser et al., 2009;Shen et al., 2009) or the utilization of document-level context (Xiao et al., 2011;Ture et al., 2012;Xiao et al., 2012;Xiong et al., 2013).

target-side dependencies attract little attention, although they have an important impact on the accuracy of lexical selection.


Here both the baseline and LRSM fail to obtain the right translation for the word "n" because "palestine" has a higher probability than "pakistan" (0.0374 vs 0.0285).

in our model, the long-distance dependencies between ("musharraf", "pakistan") and ("taliban", "pakistan") help the decoder correctly choose the translation "pakistan" for "n".


real-valued vectors, i.e., word embeddings.

translation units in machine translation have long since shifted from words to phrases (sequence of words), of which syntactic and semantic information cannot be adequately captured and represented by word embeddings.


as an additional model parameter along with the regular parameters, i.e., weights, look-up vectors.


The ability to generalize and learn complex semantic relationships (Mikolov et al., 2013b) and its compelling empirical results gives a strong motivation to use the NNJM model for the problem of domain adaptation in machine translation.

the vanilla NNJM described above is limited in its ability to effectively learn from a large and diverse out-domain data in the best favor of an indomain data.


The bias created due to the out-domain data caused S cat to choose the contextually incorrect translation unwanted pregnancy.

the adapted systems S v * were able to translate it   (How about fitness?


We were able to obtain an average gain of +0.3 BLEU points by training an NDAM v1 model over the selected data (see S v1+mml ).

on English-German, the MML-based selection caused a drop in the performance (see Table 6).


With the help of U.S. army, these soldiers are searching and suppressing members of Abu Sayyaf.


The need for identifying content-heavy sentences arises in many specialized domains, including dialog systems, machine translation, text simplification and Chinese language processing but it is usually addressed in an implicit or application specific way.

we focus on identifying heavy sentences as a standalone task, providing a unifying view of the seemingly disparate strands of prior work.


Most recently, text simplification has been addressed as a monolingual machine translation task from complex to simple language (Specia, 2010;Coster and Kauchak, 2011;Wubben et al., 2012).

simplification by repackaging the content into multiple sentences is not naturally compatible with the standard view of statistical MT in which a system is expected to produce a single output sentence for a single input sentence.


Similar to work in text simplification, the simplification rules are applied to all sentences meeting certain criteria, normally to all sentences longer than a predefined threshold or where certain conjunctions or coordinations are present.

the model we propose here can be used to predict when segmentation is at all necessary.


Particularly, Liu and Huang (2014) show that by requiring the conventional parameter tuning algorithms to consider the final decoding results (full translations) as well as the intermediate decoding states (partial translations) at the same time, the inexact decoding can be significantly improved so that correct intermediate partial translations are more likely to survive the beam.

the underlying phrase-based decoding model suffers from limited distortion, and thus, may not be flexible enough for translating language pairs that are syntactically different, which require long distance reordering.


Partial BLEU is quite an intuitive choice for evaluating partial derivations.

as Liu and Huang (2014) discussed, partial BLEU only evaluates the partial derivation itself without considering any of its context information, leading to a performance degradation.


Compared to the concatenation method, top-down is able to consider the reordering between spans, and thus would be much better.

since it bases on the k-best list in the final beam, it can only handle the spans appearing in the final k-best derivations.


For the spans that top-down or guided backtrace algorithm cannot get outside strings, we use concatenation for them to maintain consistent number of tuning instances between different tuning iterations.

since we do not want to spent much effort on them, we only use the one-best partial derivation for each of them.


In the former, the transferred tags are used to train a partially-observed CRF (PO-CRF) by maximizing the probability of a constrained lattice.

instance-based learning views each word token as an independent classification task, but uses latent distributional information gleaned from surrounding words as features.


Most of the missing assignments are for foreign proper nouns, which often do not receive case markers.

this is not done consistently in the training data we use.


The architecture is shown in Figure 1(d).

figure 1(e) directly combines discrete and continuous features, replacing the hard-coded transformation function of Guo et al.


In this sense, the method of Chen and Manning resembles a traditional supervised sparse linear model, which can be weak on OOV.

the semi-supervised learning methods such as Turian et al.


A two-stage method (McDonald, 2006) is often used because the complexity of some joint learning models is unacceptably high.

joint learning models can benefit from edge-label information that has proven to be im-portant to provide more accurate tree structures and labels (Nivre and Scholz, 2004).


Most work on unsupervised domain adaptation in NLP uses batch learning: It assumes that a large corpus of unlabeled data of the target domain is available before testing.

batch learning is not possible in many real-world scenarios where incoming data from a new target domain must be processed immediately.


With S RANK , there is very little advantage over looking at just pairs.

with S EMBED or S PMI the improvement of using the triplet-based method over using just the head-modifier pairs is clear.


These features can be integrated naturally as atomic inputs to the embedding layer of the network and the model can learn arbitrary conjunctions with all other features through the hidden layers.

integrating such features into a model with discrete features requires nontrivial manual tweaking.


Model combination techniques have consistently shown state-of-the-art performance across multiple tasks, including syntactic parsing.

they dramatically increase runtime and can be difficult to employ in practice.


Given these scores, a natural way to obtain weights is to normalize the probabilities.

parsers do not always provide accurate estimates of parse quality.


Nicely played!, the prediction of the word played, depends on both the syntactic relation from nicely, which narrows down the list of candidates to verbs, and on the semantic relation from game, which further narrows down the list of candidates to verbs related to games.

the words we and the add very little to this particular prediction.


On the other hand, the words we and the add very little to this particular prediction.

the word the is important for predicting the word game, since it is generally followed by nouns.


The continuous bag-of-words model differs from other proposed models in the sense that its complexity does not rise substantially as we increase the window b, since it only requires two extra additions to compute c, which correspond to d w operations each.

the skipn-gram model requires two extra predictions corresponding to d w × V operations each, which is an order of magnitude more expensive even when subsampling V .


On the other hand, the skipn-gram model requires two extra predictions corresponding to d w × V operations each, which is an order of magnitude more expensive even when subsampling V .

the drawback the bagof-words model is that it does not learn embeddings that are prone for learning syntactically oriented tasks, mainly due to lack of sensitivity to word order, since the context is defined by a sum of surrounding words.


(2013) and Nivre and Fernandez-Gonzalez (2014), who each present modifications to the arc-eager transition system that introduce some non-monotonic behaviour, resulting in small improvements in accuracy.

these systems only apply non-monotonic transitions to a relatively small number of configurations, so they can only have a small impact on parse accuracy.


PPDB is a natural resource for paraphrases.

ppDB was not built with the specific application to SMT in mind.


Note that the JTR source and target sides include jump information, therefore, the RNN model described above explicitly models reordering.

the models proposed in (Sundermeyer et al., 2014) do not include any jumps, and hence do not provide an explicit way of including word reordering.


Comparing between CNN and AverageSG, we can conclude that deep semantic compositionality is crucial for understanding the semantics and the sentiment of documents.

it is somewhat disappointing that these models do not significantly outperform discrete bag-of-ngrams and bag-of-features.


Afterwards, relation-specific gated RNN can be developed to explicitly model semantic composition rules for each relation (Socher et al., 2013a).

defining such a relation scheme is linguistic driven and time consuming, which we leave as future work.


The state-of-the-art model for opinion target extraction is also based on a CRF (Pontiki et al., 2014).

the success of CRFs depends heavily on the use of an appropriate feature set and feature function expansion, which often requires a lot of engineering effort for each task in hand.


One simple way to overcome this issue is to use a truncated BPTT (Mikolov, 2012) for restricting the backpropagation to only few steps like 4 or 5.

this solution limits the RNN to capture long-range dependencies.


Our window-based approach, by considering the neighboring words, already captures short-term dependencies like this from the future.

it requires tuning to find the right window size, and it disregards long-range dependencies that go beyond the context window, which is typically of size 1 (i.e., no context) to 5 (see Section 5.2).


This finding contrasts the finding of Irsoy and Cardie (2014) in opinion expression detection task, where bi-directional Elman RNNs outperform their uni-directional counterparts.

when we analyzed the data, we found it to be unsurprising because aspect terms are generally shorter than opinion expressions.


The approach is closely related to the hybrid dependency model (Clark and Curran, 2007).

the CCGbank dependencies used by Clark and Curran's model constrain all lexical and attachment decisions (only allowing 'spurious' derivational ambiguity) whereas our use of semantic dependencies models most of the syntactic parse as latent.


Unsurprisingly, verb arguments and adjuncts relations show large improvements.

we also see more accurate attachment of relative clauses.


In the SMT community, researchers have developed standard, proven alignment tools such as GIZA++ (Och and Ney, 2003), which can be used to train IBM Models 1-5.

there is one fundamental problem with the IBM models (Brown et al., 1993): each word on one side can be traced back to exactly one particular on the other word (or the null token which indicates the word aligns to no word on the other side).


Applying such string-to-tree or tree-to-tree translation models (Yamada and Knight, 2001;Shen et al., 2008) to semantic parsing will naturally resolve the inconsistent semantic structure issue, though they require additional information to generate tree labels on the target side.

due to the constraint that each target phrase needs to map to a syntactic constituent, phrase tables in tree-based translation models usually suffer from the low coverage issue, especially if the training data size is small.


Concretely, if for some literal l j , A diagram (l i ) < , then GEOS disregards the text score of l i by replacing A text (l j ) with A diagram (l j ).

even if the diagram score of a literal is very high, it is still possible that the literal is false, because many diagrams are not drawn to scale.


The rule-based text parsing baseline achieves a high precision, but at the cost of lower recall.

the baseline GEOS without diagram achieves a high recall, but at the cost of lower precision.


The efficiency gain comes from the fact that we can just do the multiplication once per path type, instead of once per path type per source node.

to correctly compute the probabilities for a (source, target) pair, we need to exclude from the graph the edge connecting that training instance.


This captures in just two features all path types of the form -ALIAS-[some textual relation]-ALIAS -1 -, and those two bigram features are almost always the highest weighted features in models where they are used.

the bigram features do not capture those path types exactly.


Additionally, we evaluate the approach on a dataset that contains rich prior information from the training knowledge base, as well as a wealth of textual information from a large document collection.

to the work reviewed so far, work on sentence-level relation extraction using direct supervision has focused heavily on representing sentence context.


In the standard latent feature models discussed above, each textual relation is treated as an atomic unit receiving its own set of latent features.

many textual relations differ only slightly in the words or dependency arcs used to express the relation.


It is commonplace to represent words as vectors.

to naïve models in which all word types in a vocabulary V are equally different from each other, vector space models capture the intuition that words may be different or similar along a variety of dimensions.


In contrast, we would like to compose representations of characters into representations of words.

the relationship between words forms and their meanings is non-trivial (de Saussure, 1916).


We can see that even without feature engineering or unsupervised pretraining, our C2W model (row "C2W") is on par with the current state-of-the-art system (row "structReg").

if we add hand-crafted features, we can obtain further improvements on this dataset (row "C2W + features").


Formally, the cost function is defined as: where s 1 , s 2 are the input sentences, f the compositional layer (so f (s 1 ) and f (s 2 ) refer to sentence vectors), and y = 1 denotes a paraphrase relationship between the sentences; m stands for the margin, a hyper-parameter chosen in advance.

the cost function based on cosine similarity handles only directional differences, as follows: where is the cosine similarity of the two sentence vectors, w and b are the scaling and shifting parameters to be optimized, σ is the sigmoid function and y is the label.


The small gains of the prior disambiguation models over the ambiguous models clearly show that deep architectures are quite capable of performing this elementary form of sense selection intrinsically, as part of the learning process itself.

the situation changes when the dynamic disambiguation framework is used, where the gains over the ambiguous version become more significant.


This approach assumes each post belongs to a different topic in the thread.

to RBT, ART contains too many topic branches, one per post in the thread.


This yields collection-specific background topics by using a binomial distribution instead of a multinomial.

we prefer the simpler, non-tree version because background topics are unnecessary when using an asymmetric α prior (Wallach et al., 2009a).


Held-out perplexity on real data provides a quantitative evaluation of our models' performance in a real-world setting.

the goal of our models is to enable a deeper analysis of large, weakly-related corpora, which we next discuss.


In addition, Kim (2014) combines CNNs of different filter widths and either static or fine-tuned word vectors.

to the traditional CNN models, our method considers non-consecutive n-grams thereby expanding the representation capacity of the model.


For example, the convolutional model with multi-channel (CNN-MC) runs over 2400 seconds per training epoch.

our full model (with 3 feature layers) runs on average 28 seconds with only root labels and on average 445 seconds with all labels.


One straightforward way to compare them is to flatten the sentence representations into two vectors, then use standard metrics like cosine similarity.

this may not be optimal because different regions of the flattened sentence representations are from different underlying sources (e.g., groups of different widths, types of pooling, dimensions of word vectors, etc.).


The posterior distribution of run length P (r t |x 1:t ) can be computed recursively: where: The joint distribution over run length r t and data x 1:t can be derived by summing P (r t , r t−1 , x 1:t ) over r t−1 : This formulation updates the posterior distribution of the run length given the prior over r t from r t−1 and the predictive distribution of new data.

the existing BO-CPD model (Adams and MacKay, 2007) specifies the conditional prior on the change point P (r t |r t−1 ) in advance.


If the same data is given, BO-CPD gives us the same answer to a question whether an abrupt change at time t is a change point or not.

dBO-CPd uses documents d γ t for its prediction to incorporate the external information which cannot be inferred only from the data.


In recent work, some research explore "deep" expressions such as discourse commitments or strict logic for representing the text.

these expressions suffer from the limitation of inference inconvenience or translation loss.


Through logic inference, some implicit knowledge behind the text can be mined.

it is not easy to translate the natural language text into formal logic expressions and the translation process inevitably suffer from great information loss.


With the result in Table 2, it is true that these pre-trained word embeddings have a good effect on our performance (we use word2vec 3 on Chinese Gigaword Corpus for word pre-training).

as shown in Table 2, compared to standard pre-training, the influence of heterogenous data is more evident.


More recently, Zhou and Xu (2015) proposed L-STM RNN approach for English Semantic Role Labeling, which shared similar idea with our model.

the features used and the network architecture were different from ours.


Due to the commonality in natural language, negation focus plays a critical role in deep understanding of context.

existing studies for negation focus identification major on supervised learning which is timeconsuming and expensive due to manual preparation of annotated corpus.


In this paper, we thus focus on graph-based ranking methods (Mihalcea and Tarau, 2004) which first build a word graph according to word co-occurrences within document, and then use random walk algorithms (e.g., PageRank) to measure word importance.

for negation focus identification, the graph-based methods may suffer from the following two problems: (a) the words in graphbased methods are strongly connected by cooccurrence rather than semantic content, which do not necessarily guarantee that they are relevant to the negation focus in context; and (b) identifying a negation focus may be affected by not only the relatedness of surrounding words but also its importance in current document which is not considered in standard random walk algorithms.


Vector space models of language like the ones presented in (Collobert et al., 2011b;Mikolov et al., 2013;Pennington et al., 2014) create good representations for the individual words of a language.

the words in a language can be combined into infinitely many distinct, wellformed phrases and sentences.


Interpretation of events-determining what the author claims did or did not happen-is important for many NLP applications, such as news article summarization or biomedical information extraction.

detecting events and assessing their factuality is challenging.


(2012), who advocated for representing factuality from the reader's perspective as a distribution of categories, but their annotation process requires manual normalization of the text.

we model factuality from the author's perspective with scalar values, and we have an endto-end crowdsourced annotation pipeline.


A prerequisite relation describes a basic relation among concepts in cognition, education and other areas.

as a semantic relation, it has not been well studied in computational linguistics.


adverse drug reactions, infectious diseases) in particular communities.

in order for a machine to understand and make inferences on these health conditions, the ability to recognise when laymen's terms refer to a particular medical concept (i.e.


We firstly observe that for the vSim baseline, excepting for word vector representation with vector size 50 learned using GloVe from the Twitter collection, word vector representations learned using either CBOW or GloVe are more effective than the one-hot representation.

the difference between the MRR-5 performance is not statistically significant (p > 0.05, paired t-test).


We argue that some types are relatively simple to answer, partly due to the limited vocabulary used, which explains why simple lexical matching methods can perform well.

some questions require understanding of higher level concepts such as those of the story and its characters, and/or require inference.


Then we attempted to account for them by searching the text for the sentence indicating that she had gone home and reducing the weight for all subsequent sentences.

since the improvements due to these rules were negligible, we did not include them in our final system.


(2013) demonstrate that the MC160 and MC500 have similar ratings for clarity and grammar, and that humans perform equally well on both.

in many cases MC500 appears to be designed in such a way to confuse lexical algorithms and encourage the use of more sophisticated techniques necessary to deal with phenomena such as elimination questions, negation, and common knowledge not explicitly written in the story.


Existing learning algorithms have primarily focused on building actionable meaning representations which can, for example, directly query a database (Liang et al., 2011;Kwiatkowski et al., 2013) or instruct a robotic agent (Chen, 2012;Artzi and Zettlemoyer, 2013b).

due to their end-to-end nature, such models must be relearned for each new target application and have only been used to parse restricted styles of text, such as questions and imperatives.


AMR meaning bank provides a large new corpus that, for the first time, enables us to study the problem of grammar induction for broad-coverage semantic parsing.

it also presents significant challenges for existing algorithms, including much longer sentences, more complex syntactic phenomena and increased use of noncompositional semantics, such as within-sentence coreference.


Given the partial derivations, our gradient computation is identical to Equation 2.

in contrast to Collins and Roark (2004) our data does not include gold derivations.


To minimise handcrafting, Stent and Molina (2009) proposed learning sentence planning rules directly from a corpus of utterances labelled with Rhetorical Structure Theory (RST) discourse relations (Mann and Thompson, 1988).

the required corpus labelling is expensive and additional handcrafting is still needed to map the sentence plan to a valid syntactic form.


Paliwal, 1997) have been shown to be effective for sequential problems (Graves et al., 2013a;Sundermeyer et al., 2014).

applying a bidirectional network directly in the SC-LSTM generator is not straightforward since the generation process is sequential in time.


In addition, the slot error rate (ERR) as described in Section 3.5 was computed as an auxiliary metric alongside the BLEU score.

for the experiments it is computed at the corpus level, by averaging slot errors over each of the top 5 realisations in the entire corpus.


For every task, the +Expectation method has performances that often seem to be higher than the simple baseline (both for the 50d case or the 100d case).

only some of these differences are significant.


The system described in (Hosseini et al., 2014) handles only addition and subtraction problems, and requires additional annotated data for verb categories.

our system does not require any additional annotations and can handle a more general category of problems.


It tries to map numbers from the problem text to predefined equation templates.

they implicitly assume that similar equation forms have been seen in the training data.


However, they implicitly assume that similar equation forms have been seen in the training data.

our system can perform competitively, even when it has never seen similar expressions in training.


We believe that the improvement was mainly due to the dependence of the system of (Roy et al., 2015) on lexical and neighborhood of quantity features.

features from quantity schemas help us generalize across problem types.


First, in this method, an already existing knowledge base is heuristically aligned to texts, and the alignment results are treated as labeled data.

the heuristic alignment can fail, resulting in wrong label problem.


These methods have been shown to be effective for relation extraction.

their performance depends strongly on the quality of the designed features.


successfully incorporated multiinstance learning into traditional Backpropagation (BP) and Radial Basis Function (RBF) networks and optimized these networks by minimizing a sum-of-squares error function.

to their method, we define the objective function based on the cross-entropy principle.


The idea is to capture the most significant features (with the highest values) in each feature map.

despite the widespread use of single max pooling, this approach is insufficient for relation extraction.


Here we considered a fact or tuple as linked if both of its entities were linked to Freebase, as partiallylinked if only one of its entities was linked, and as non-linked otherwise.

to previous work (Riedel et al., 2013;, we retain partially-linked and non-linked facts in our dataset.


Other words with embeddings similar to "driving" that appear on the dependency path between the mentions will similarly receive high weight for the ART label.

if the embedding is similar but is not on the dependency path, it will have 0 weight.


Roth and Woodsend (2014) considered features similar to ours for semantic role labeling.

in prior work both of above approaches are only able to utilize limited information, usually one property for each word.


As we can see, POS tags, grammatical relations, and WordNet hypernyms are also discrete (like words per se).

no prevailing embedding learning method exists for POS tags, say.


The Bayesian model of X&T is important in providing insight into how people might reason about samples of data that exemplify categories.

it relies on having complete, built-in knowledge about the taxonomic hierarchy, including both the detailed composition of categories and the values for between-object similarities, drawn from adult similarity judgments.


In the FAS model, all the features for a word are dependent: increasing the probability of any feature results in decreasing the probability of others.

this interaction is not always desirable, as many features regularly co-occur in the world.


conditions), the model, like children, generalizes to the lowest level category in the hierarchy that is consistent with the training items, roughly equally preferring items from that category or lower, with slight preference for the lower categories.

after seeing a single training example (the (a) Adult data: 3 subord.


(2013) present latent variable models for unsupervised learning of latent character types in movie plot summaries and in English novels, taking authorial style into account.

even the state-of-the-art NLP work rather describes personas of fictional characters by their role in the story -e.g., action hero, valley girl, best friend, villain etc.


This poses a challenge for fictional characters.

strong correlations have been found between the self-reported and perceived personality traits (Mehl et al., 2006).


We have shown that exploiting the links between lexical resources to leverage more accurate semantic information can be beneficial for this type of tasks, oriented to actions performed by the entity.

the human annotator agreement in our task stays high above the performance achieved.


Expectation-maximization algorithms, such as those implemented in GIZA++ pervade the field of unsupervised word alignment.

these algorithms have a problem of over-fitting, leading to "garbage collector effects," where rare words tend to be erroneously aligned to untranslated words.


They associate two models via the agreement constraint and show that agreement-based joint training improves alignment accuracy significantly.

enforcing agreement in joint training faces a major problem: the two models are restricted to one-to-one alignments (Liang et al., 2006).


propose a joint model to process word segmentation and informal word detection.

text normalization is split to another task ).


Their model is trained on a partially annotated microblog corpus.

our model can be trained on existing annotated corpora in standard text.


release a Chinese microblog corpus for word segmentation and informal word detection.

there are no microblog corpora annotated Chinese word segmentation, POS tags, and normalized sentences.


A Naive h function is straightforwardly defined as the annotation of each local rule part of the tree in a bottom-up fashion: 1 Base: When ∃a ∈ A such that h(a) = ⊥ we say that the annotation has failed.

the naive procedure fails in a large number of cases.


Given a grammar in CNF, we can prove that for a sentence of length n, the number of derivation steps for a shift reduce parser is 3n − 1.

our tagset-preserving transformation also introduces rules of the form (b), which explains why the number of derivation steps may vary from 2n − 1 to 3n − 1.


This approach induces a compact feature representation by combining atomic features.

unlike traditional tensor models, it enables us to incorporate prior knowledge about desired feature interactions, eliminating invalid feature combinations.


These gains were obtained by a careful construction of features templates that combine standard dependency parsing features and typological features.

we propose an automated, tensor-based approach that can effectively capture the interaction between these features, yielding a richer representation for crosslingual transfer.


While these methods can automatically combine atomic features into a compact composite representation, they cannot take into account constraints on feature combination.

our method can capture features at different composition levels, and more generally can incorporate structural constraints based on prior knowledge.


In addition, we consider the semi-supervised transfer scenario, in which we assume 50 sentences in the target language are available with annotation.

we observe that random sentence selection of the supervised sample results in a big performance variance.


We believe that the reason is two-fold: (a) k-means finds a local maximum during clustering; (b) we do hard clustering instead of soft clustering.

we detected that the clustering algorithm gives a more diverse set of solutions, when the features are perturbed.


2013; Choi and McCallum, 2013; Ballesteros and Bohnet, 2014).

these methods are based on discrete features and suffer from the problems of data sparsity and feature engineering (Chen and Manning, 2014).


Le and Zuidema (2014) proposed a generative re-ranking model with Inside-Outside Recursive Neural Network (IORNN), which can process trees both bottom-up and top-down.

iORNN works in generative way and just estimates the probability of a given tree, so iORNN cannot fully utilize the incorrect trees in k-best candidate results.


This is the case of many application like message dictation for example.

in the case of service at hand, an initial concept must be detected (the action), therefore, FAIL RAW improves the performance.


Moreover, continuous representation of words in neural network LMs can also support robust modeling (Bengio et al., 2003;Mikolov et al., 2010).

previous works are focused on maximizing performance in the same domain as that of the training data.


In other words, it is uncertain that these technologies robustly support out-of domain tasks.

latent words LMs (LWLMs) (Deschacht et al., 2012) are clearly effective for outof domain tasks.


LW is comparable to MKN and HPY, and inferior to RNN in terms of PPL.

in test sets (out-of domain tasks), PPL improved with the increase in the number of layers in LW.


We use the following features in our implementation of this model.

any relevant ASR and SMT feature may be readily added to this model.


As this problem is NP-hard, pruning low-weight concepts is required for the ILP solver to find optimal solutions efficiently Li et al., 2013).

reducing the number of concepts in the model has two undesirable consequences.


fluency, should be conducted using manual evaluation.

conducting formal human evaluation is somewhat problematic.


Initially, manual evaluation was carried out, where human judges were tasked to assess the quality of automatically generated summaries.

in an effort to make evaluation more scaleable, the automatic ROUGE 1 measure (Lin, 2004b) was introduced in DUC-2004.


In this variant, word embeddings are used, as we are proposing in this paper, to map text content within generated summaries to SCUs.

the SCUs still need to be manually identified, limiting this variant's scalability and applicability.


The MDL principle is widely useful in compression techniques of non-textual data, such as summarization of query results for OLAP applications.

(Lakshmanan et al., 2002;Bu et al., 2005) only a few works on text summarization using MDL can be found in the literature.


It is noteworthy that, in addition to the greedy approach, we also evaluated the global optimization with maximizing coverage and minimizing redundancy using Linear Programming (LP).

experimental results did not provide any improvement over the greedy approach.


Our work is based on the bipartite entity graph introduced by Guinaudeau and Strube (2013).

in their graph one set of nodes corresponds to entities whereas in our graph it corresponds to topics.


We use the DUC 2002 dataset to compare our results with state-of-the-art techniques.

to the PLOS Medicine data the DUC 2002 dataset contains very small articles.


However the difference between the results of Tgraph and Egraph are not significant.

to the entity graph based system, the coherence measure in our system is calculated by using a topic-based weighted projection graph, which is denser and hence more informative.


The results are comparable with the entity graph.

the entity graph is less informative and very sparse as compared to the topical graph.


Instead of performing SVD, we can also take s i ∈ R B as our sentence representation, which makes our method resemble the bigram coverage-based summarization approach.


Colmenares et.al construct a 1.3 million financial news headline dataset written in English for headline generation (Colmenares et al., 2015).


Recently, recurrent neural network (RNN) have shown powerful abilities on speech recognition (Graves et al., 2013), machine translation  and automatic dialog response (Shang et al., 2015).

there is rare research on the automatic text summarization by using deep models.


The first and second tier cities are facing growth difficulties.

o2o market in the third and fourth tier cities contains opportunities.


To some extent, this hope was validated through a number of works at the time, mostly involving machine translation applications, and constraining in more or less explicit ways the specification of r (van Noord, 1990).

for the non-statistical approaches to parsing then strongly dominant, robustness was an issue: a parser had to either accept or reject a given input x, with no intermediary options, and in order to be able to parse actual utterances, with all their empirical diversity, parsers had to be rather tolerant.


Modern QA systems rely on an independent component to pre-select candidate answer sentences, which utilizes various signals such as lexical matching and user behaviors.

the candidate sentences  Table 5: Evaluation of answer triggering on the WIKIQA dataset.


Most work leverages multiple sources of information, such as search query history, Twitter feeds, Facebook likes, social network links, and user profiles.

in many situations, little of this information is available.


We term this procedure as On-Demand Augmentation (ODA), because the search can be performed during test time in an on-demand manner.

the previous approaches of adding edges or embeddings to the KB (Gardner et al., 2013), and vector space random walk PRA (Gardner et al., 2014) are batch procedures.


Using surface level relations and noun phrases for extracting meaningful relational facts is not a new idea (Hearst, 1992), (Brin, 1999), (Etzioni et al., 2004).

none of them make use of Knowledge Bases for improving information extraction.


It is then reasonable to expect that a state-of-the-art formal semantics provides an accurate computational basis of natural language inferences.

there are still obstacles in the way of achieving this goal.


Thus, E2 and E4 are relatively difficult to be recognized as events by themselves.

event coreference E1-E2, which is supported primarily by E2's participants Barclays and Zaragozano shared with E1, helps determine that E2 is an event.


That is, one can train a structured learning model to globally capture the interactions between two relevant tasks via a certain kind of structure, while making predictions specifically for these respective tasks.

no prior work has studied the interactions between event trigger identification and event coreference resolution.


If one uses this approach, a beam state may represent a partial assignment of an event trigger.

event coreference can be explored only from complete assignments of an event trigger.


It is possible for the model to predict an invalid encoded sequence that does not correspond to any word in the original vocabulary.

in our experiments, we did not observe any such sequences in the decoding of the test set.


the whole neural network (not just the output layer like the NNJM) for each noise sample and thus noise computation is more expensive.

for different epochs, we resampled the negative example for each positive example, so the BNNJM can make use of different negative examples.


For the number of top configurations k used to initialize each following stage, we know the larger k is, the better results in the next stage since Bayesian Optimization relies on good initial knowledge to fit good regression models (Feurer et al., 2015).

larger k value also leads to high computation cost at the next stage, since these initial settings will have to be evaluated first.


Notice that the weakly supervised classifier trained with 68M words obtains 68% accuracy on the AOC test set and 48% on the FB test set (row 1), which is not much higher.

considering this classifier is trained without any human labeled dialect data, the performance is expected and can be improved with better training data and models.


Precisions increase from the weakly supervised to the strongly supervised to the semi-supervised classifier, and the combined classifier generally outperforms all three classifiers, except for the Gulf dialect.

considering the smaller percentage of the Gulf dialect, we still observe significant improvement overall.


Recognizing claims exhibits similar behavior.

recognizing non-argumentative text performs better in the opposite direction.


These papers draw similar conclusions, showing that the the distribution of geotagged tweets over the US population is not random, and that higher usage is correlated with urban areas, high income, more ethnic minorities, and more young people.

this prior work did not consider the biases introduced by relying on geotagged messages, nor the consequences for geo-linguistic analysis.


As described in Section 3.2, the distant supervision labels were based on a linear combination of three heuristics that achieved at best an RMSE of 5.1 sentences.

with self-training, we can exploit the noisy heuristic labels by using only those labels that agree with the seed-trained model, thus reducing the amount of noise.


In this work, we also consider sentences as units of suggestion.

we observe that sentences might miss the context, or refer to something mentioned in the previous sentence.


And reposting messages, namely reposts, can provide valuable context information to the previous posts including their background, development, public opinions and so on.

a popular post usually attracts a large number of reposts.


A simple way to detect leaders on repost tree is to directly apply a binary classifier like SVM on each individual message.

these models assume reposts are independent without effectively leveraging abundant context along the repost tree paths, such as the reposting relations among different reposts on a path.


Therefore, this makes it possible to include true leaders misclassified as followers by leader detection module into summary.

allowing all messages to participate in ranking also increases the risk of selecting real followers.


Traditional approaches to zero anaphora resolution are based on manually created heuristic rules (Kameyama, 1986;Walker et al., 1994;Okumura and Tamura, 1996;Nakaiwa and Shirai, 1996), which are mainly motivated by the rules and preferences introduced in Centering Theory (Grosz et al., 1995).

the research trend of zero anaphora resolution has shifted from such rule-based approaches to machine learningbased approaches because in machine learning we can easily integrate many different types of information, such as morpho-syntactic, semantic and discourse-related information.


In these methods, the semantic compatibility between the contexts surrounding an anaphor and its antecedent (e.g., the compatibility of verbs kidnap and release given some arguments) was automatically extracted from raw texts in an unsupervised manner and used as features in a machine learning-based approach.

because the automatically acquired semantic compatibility is not always true or applicable in the context of any pair of an anaphor and its antecedent, the effectiveness of the compatibility features might be weakened.


Another important point is that in the NAIST Text Corpus, if the antecedent of a zero anaphor is not explicitly written in the corpus, it is simply annotated as 'exophoric', and the subject sharing relations between two predicates whose subject was annotated as exophoric cannot be captured.

in our cleaning procedure, the annotators additionally annotated such 'exophoric' subject sharing relations to take into account all subject sharing relations in the corpus.


When combining more than one subject sharing recognizer in our method, we construct the SSPN using the subject sharing relations recognized by at least one of those recognizers for transitive subject propagation.

in the baseline method, the SSPN was not constructed and zero anaphoric relations were identified using only the outputs of our subject detector and one of those recognizers.


The results are shown in Table 5 and demonstrate that the performance of all the methods without Step 5 does not reach that of the baseline method in F-score.

they retain high precision that ranges from 60% to 75%, preserving more than 10% of the recall on the DEP, DEP+ADJ, DEP+PNP and DEP+ADJ+PNP methods.


Given labeled data, supervised learning can be applied to obtain sentiment weights for each word.

the effectiveness of supervised sentiment analysis depends on having training data in the same domain as the target, and this is not always possible.


While this neural network structure captures the idea of compositionality over the RST tree, the most deeply embedded discourse units can be heavily down-weighted by the recursive composition (assuming K s < K n ): in the most extreme case of a right-branching or left-branching structure, the recursive operator may be applied N times to the most deeply embedded EDU.

discourse depth reweighting applies a uniform weight of 0.5 to all discourse units with depth ≥ 3.


Sentiment polarity analysis has typically relied on a "preponderance of evidence" strategy, hoping that the words or sentences representing the overall polarity will outweigh those representing counterpoints or rhetorical concessions.

with the availability of off-the-shelf RST discourse parsers, it is now easy to include documentlevel structure in sentiment analysis.


Expert-annotated datasets of implicit discourse relations are expensive to produce, so it would be preferable to use weak supervision, by automatically labeling instances with explicit connectives (Marcu and Echihabi, 2003).

sporleder and Lascarides (2008) show that models trained on explicitly marked examples generalize poorly to implicit relation identification.


2 Related Work Marcu and Echihabi (2003) train a classifier for implicit intra-sentence discourse relations from explicitly-marked examples in the rhetorical structure theory (RST) treebank, where the relations are automatically labeled by their discourse connectives: for example, labeling the relation as CONTRAST if the connective is but.

sporleder and Lascarides (2008) argue that explicitly marked relations are too different from implicit relations to serve as an adequate supervision signal, obtaining negative results in segmented discourse representation theory (sDRT) relations.


The simplest way is taking the intersection of the corresponding constraints.

we should consider the fact that the properties assigned automatically can be erroneous, since none of the analyzer is perfect.


Maybe RAE needs more labeled training data for better results.

sCNN models perform remarkably well, producing comparable and even better results.


A promising result for language learning has been shown in (Yu and Siskind, 2013) and a quite challenging effort to describe cooking activities was made in (Regneri et al., 2013).

these studies rely only on visual information, while we aim to build a system that is able to describe everyday activities using multimodal information.


Other options for representing text structure such as full-text discourse parsers (Marcu, 2000) may be not available or don't have satisfied performance, especially for non-English languages.

modeling local coherence alone is not adequate to distinguish discourse elements in persuasive essays.


Our method provides a promising solution when retraining a system is impossible or difficult.

it may raise a question of the computing cost for tuning penalty scores especially with the large number of constraints.


This leads to a drop in lemmatization performance in all languages except Spanish (English has no additional attributes).

preliminary experiments showed that correct morphological attributes would substantially improve lemmatization as they help in cases of ambiguity.


In their case, the best results produced an accuracy of 93%.

their system is augmented with a dictionary, and the distribution of accents and grammatical behaviour are also quite different from Hungarian.


Sometimes such forms could be the correct ones, but the more productive compounding and derivation there is in a word, the lower score it should get.

the frequencies of the lemma and the inflectional pattern should increase the score of a candidate, thus these components were given positive weights.


Morphological analysis including word segmentation has been widely and actively studied, and for example, Japanese word segmentation accuracy is in the high 90s.

we often observe that strange outputs of downstream NLP applications such as machine translation and question answering come from incorrect word segmentations.


That is, if high-quality morphological analysis is available, we can learn a high-quality language model from a morphologically analyzed large corpus.

if a high-quality language model is available, we can achieve highquality morphological analysis by looking for a segmented word sequence with a large language model score.


The RNNLM is trained on an automatically analyzed corpus of ten million sentences, which possibly includes incorrect segmentations such as " (foreign)/ (carrot)/ (regime)."

on semantically generalized level, it is an unnatural semantic sequence like nation vegetable politics.


The recurrent model makes decoding harder than nonrecurrent neural network language models.

we use RNNLM because the model outperforms other NNLMs (Mikolov, 2012) and the result suggests that the model is more likely to capture semantic plausibility.


Murawaki and Kuro-hashi (2008) proposed an online method in a similar setting.

to these studies, this paper proposes to use other modalities, game states as the first trial, than languages.


For example, a verb and its corresponding direct object can be far away in terms of tokens if many adjectives lies in between, but they are adjacent in the parse tree (Irsoy and Cardie, 2013).

we do not know if this advantage is truly important, and if so for which tasks, or whether other issues are at play.


RNNs encode, to some extent, structural information by recursive semantic composition along a parse tree.

they may have difficulties in learning deep dependencies because of long propagation paths (Erhan et al., 2009).


Bag-of-words models A simple and intuitive method is the Neural Bag-of-Words (NBOW) model, in which the representation of sentences or documents can be generated by averaging constituent word representations.

the main drawback of NBOW is that the word order is lost.


In many of these settings, the polygraph test has been used as the main method to identify deceptive behavior.

this method requires the use of skin-contact devices and human expertise, making it infeasible for large-scale applications.


For example, the interviewer asks a random individual on his opinion on a non-existing film where the interviewee fabricates a story.

truthful videos are collected from individuals asked on their opinions on real movies.


Keyword search is often used as the first step.

that is not sufficient due to low precision and low recall.


For example, in an application, the training data has no negative examples about sports.


In order to balance between model over-fitting and under-fitting, Tax and Duin (2001) proposed a method that tries to use artificially generated outliers to optimize the model parameters.

their experiments suggest that the procedure to generate artificial outliers in a hyper-sphere is only feasible for up to 30 dimensions.


A fully supervised baseline that uses 100% of the training set achieves an F1-score of 0.720 (using content) and 0.738 (using citation contexts).

co-training requires only 15% of the labeled training set to outperform the fully supervised content baseline and 30% of the training set to outperform the fully supervised citation contexts baseline.


As can be seen in the figure, overall, the co-training approach significantly outperforms both variations of EM.

the co-training method falls short when using 5% of the training instances, where EM Content and EM Citations methods are achieving higher F1-score values.


For example, words like learning, multi-agent or interface are more important in the content view.

words such as document or text achieve a higher information gain score for the citation contexts view.


Most puns are well structured and play with contrasting or incongruous meaning.

humor sentences in the 16000 One Liners often rely on the reader's awareness of attention-catching sounds (Mihalcea and Strapparava, 2005).


This may be evidence that the two communities have already independently identified appropriate dimensionality reduction techniques for their respective data sources.

our results support that the speech community can benefit from broader use of sparsity-inducing graphical models such as SAGE in tasks like spoken topic discovery and recommendation, in which humaninterpretable representations are desired.


Many model components of competitive statistical machine translation (SMT) systems are based on rather simplistic definitions with little linguistic grounding, which includes the definitions of phrase pairs, lexicalized reordering, and n-gram language models.

earlier work has also shown that statistical MT can benefit from additional linguistically motivated models.


Ge (2010) captures reordering patterns by defining soft constraints based on the currently translated word's POS tag and the words structurally related to it.

target syntax is more challenging to use in PBSMT, since a target-side syntactic model does not have access to the whole target sentence at decoding.


As for the varying features defining different BiSLM versions, we again see little effect of the labeling type or subtree completeness definition.

we see the opposite pattern for the unalign-adjoin feature, where unalign-adjoin+ is preferred.


As always, there is a trade-off between accuracy, space, and time, with recent papers considering small but approximate lossy LMs (Chazelle et al., 2004;Talbot and Osborne, 2007;Guthrie and Hepple, 2010), or loss-less LMs backed by tries (Stolcke et al., 2011), or related compressed structures (Germann et al., 2009;Heafield, 2011;Pauls and Klein, 2011;Sorensen and Allauzen, 2011;Watanabe et al., 2009).

none of these approaches scale well to very high-order m or very large corpora, due to their high memory and time requirements.


For 2-grams, D-CST is 3 times slower than a 2-gram SRILM index as the expensive N 1+ ( • α • ) is not computed.

for large mgrams, our indexes are much slower than SRILM.


(2010) defined three single similarities (i.e., Name similarity, Profile similarity and Structural similarity) based on the descriptions of an entity, then they employed a harmony-based method to aggregate the single similarities to get a final similarity for extracting the final mappings.

treating different kinds of descriptions of an entity separately suffers from two limitations.


And in the classifying phase, each pair of elements from two to-be-matched ontologies is predicted as matched or not according to its attributes.

eR-SOM is an unsupervised approach, but it does not exclude using external resources and training data to help learning the representations of entities and provide the initial similarity matrix for the SP method to further improve the performance.


DEP performs slightly worse than NG2 on CWS and Cilin in P@1 and P@5.

it achieves better results on Cilin in P@10 to P@100 when more candidate similar words are evaluated.


However, it achieves better results on Cilin in P@10 to P@100 when more candidate similar words are evaluated.

nG5 and nG2 mix more semantically related words.


These results show that dependency embeddings are relatively weak for answering analogy questions.

the performance also varies across different relation types.


Other WSI approaches use various forms of clustering techniques.

previous studies of the intrinsic dimensionality of distributional semantic spaces using fractal dimensions indicate that neighborhoods in semantic space have a filamentary rather than clustered structure (Karlgren et al., 2008).


Although feature norms have also been used, raw image data has become the de-facto perceptual modality in multi-modal models.

if the objective is to ground semantic representations in perceptual information, why stop at image data?


In the case of the full datasets this difference is only marginal, which is to be expected given how few of the words in the datasets are auditory-relevant.

the results indicate that adding auditory input even for words that are not directly auditoryrelevant is not detrimental to overall performance.


habitualLikewise, we mark modalized sentences as habitual if they have a strong implicature that an event has actually happened regularly (Hacquard, 2009), as in (11).

(7) is static as it does not imply that Mary actually swims regularly.


Our model would presumably benefit from a similar coupling mechanism which we could enforce as a constraint in the ILP.


In the data used in (Dong et al., 2014a), one sentence contains only one aspect.

two or more aspects can be appeared in one sentence in SemEval 2014 data.


There have been several successful attempts at sentiment polarity detection in the past (Turney, 2002;Pang et al., 2002;Pang and Lee, 2004;Mohammad et al., 2013;Svetlana Kiritchenko and Mohammad, 2014).

prediction of star ratings still considered as a challenging task (Qu et al., 2010;Gupta et al., 2010;Boteanu and Chernova, 2013).


They exploited linguistic knowledge available in the corpora to compute similarity between adjectives.

their approach did not consider polarity orientation of adjectives, they provided ordering among non-polar adjectives like, cold, lukewarm, warm, hot.


3 To rule out that the lack of any syntactic information (which human annotators use) disadvantages the model, we also experimented with including dependency triples (dobj and nsubj, the most frequent dependencies) using the Stanford Parser (Klein and Manning, 2003).

performance did not improve, so due to limited space, we did not further explore this option.


Slightly more than 5% of ratings are more than two steps off.

comparing individual annotator ratings instead of mean ratings, some crowdsource annotators are a full nine steps off, and in a single case, even one of the trained annotators was eight steps off.


The aligned-distribution results also indicate that the model is biased towards mean ratings: MAE improves for author labels, since the relatively high variation is eliminated, but worsens for the annotator labels, as variance increases.


M AE M values are all similar across languages, again confirming what has been observed on agreement.

m AE m values on experiments are sensibly worse than those measured on agreement, possibly due to the fact that we used very basic features, with limited use of sentimentrelated information.


Given a training corpus with hand-annotated sentiment polarity labels, following Kim (2014), we train a deep convolutional neural network (CNN) on it.

instead of using it as a classifier, as Kim did, we use the values from its hidden layer as features for a much more advanced classifier, which gives superior accuracy.


The table shows that the best results were obtained for textual modality; the visual modality performed worse, and the audio was least useful.

even the worst of our results is much better than the state-of-the-art (Pérez-Rosas et al., 2013).


In case of the decision level fusion experiment, the coupling of Sentic Patterns to determine the weight of textual modality has enriched the performance of multimodal sentiment analysis framework considerably.

the parameter selection for decision level fusion produced suboptimal results.


Fortunately, if we have the global context of good like interesting or amazing, the sentiment meaning of the embedding will be explicit.

the training of log-linear neural language model is based on local word dependencies (e.g., the co-occurrence of the words in a local window).


That is because PV-DBOW tends to regard ibm and mac both as computers.

the two different computer brands are distinguished in Glo-PV-DBOW.


They concluded that false rumours are more likely to receive a comment with link to Snopes.com website.

none of the above attempted to automatically classify rumours.


Perfect precision was found for claims of renewable freshwater for which one textual pattern was responsible for all the claims identified and it was correct.

the zero precision for claims of internet user % was due to identifying correctly sentences listing countries and their respective values for this property but not identifying the country-value pairs correctly.


As explained, we tackle claim identification as an instance of information extraction, and propose a baseline able to perform both tasks.

it is important to distinguish between them.


On the other hand, after the submission of our paper we became aware of a parallel work (Coavoux and Crabbé, 2016) that also proposed a dynamic oracle for their own incremental constituency parser.

it is not optimal due to dummy non-terminals from binarization.


In GHKM, a tree fragment and a sequence of words are extracted together if they are minimal and their word alignments do not fall outside of their respective boundaries.

given that alignment violations are not allowed, the quality of the extracted rules degrades as the rate of misaligned words increases.


(2012) extended the previous method with dual decomposition and HPSG parsing.

to these symmetry-directed efforts, Kawahara et al.


Note that DEP and LEN are closely related; generally center-embedded constructions are accompanied by longer dependencies so LEN also penalizes center-embedding implicitly.

the opposite is not true and there exist many constructions with longer dependencies without center-embedding.


(Future) Second, we intentionally created the EventStatus corpus to concentrate on one particular event frame (class of events): civil unrest.

previous temporally annotated corpora focus on a wide variety of events.


Example 3 In the statement from Example 1, the extraction patterns capture the dependency path connecting the head words: Iraq, administrator and Paul Bremer.

to capture the contextual information, further qualification of the argument node, administrator, is required.


NESTIE uses an approach similar to OLLIE and WOE to learn dependency parse based syntactic patterns.


Linking an incorrect proposition generates more incorrect propositions which hurt the system performance.

we hope this problem can be alleviated to some extent as parsers become more robust.


It is a fundamental task which can serve as a pre-existing system and provide prior knowledge for information ex-traction, natural language understanding, information retrieval, etc.

automatic recognition of semantic relation is challenging.


Recursive Neural Network (RNN) (Socher et al., 2012) and Convolutional Neural Network (CNN) (Zeng et al., 2014) have proven powerful in relation classification.

to traditional approaches, neural network based methods own the ability of automatic feature learning and alleviate the problem of severe dependence on human-designed features and kernels.


In contrast to traditional approaches, neural network based methods own the ability of automatic feature learning and alleviate the problem of severe dependence on human-designed features and kernels.

previous researches (Socher et al., 2012) imply that some features exploited by traditional methods are still informative and can help enhance the performance of neural network in relation classification.


These models are still ambiguous to some degree, for example when an O-node has two child nodes and two parents, we cannot decide which of the parent node is paired with which child node.

in this paper we argue that: • This model is less ambiguous compared to the linear-chain model, as we will show later theoretically and empirically.


The last example shows a very hard case of overlapping 2 It is tempting to just ignore these entities since the N type does not convey any specific information about the entities in it.

due to the dataset size, excluding this type will lead to very small number of interactions between types.


In addition, our analysis shows that the AB model produces a significant positive correlation with the PD acceptability rating.

the AB model has no correlation with the verb bias score.


Specifically, let − → f () and ← − f () be the forward and backward recurrent unit, respectively, then Independent but context dependent selection of words is often sufficient.

the model is unable to select phrases or refrain from selecting the same word again if already chosen.


Neural network based models have achieved impressive results on various specific tasks.

in previous works, most models are learned separately based on single-task supervised objectives, which often suffer from insufficient training data.


LSTM has an internal memory to keep useful information for specific task, some of which may be beneficial to other tasks.

it is non-trivial to share information stored in internal memory.


", which has a negative sentiment, while the standard LSTM gives a wrong prediction due to not understanding the informative words "cookie-cutter" and "cut-and-paste".

our model makes a correct prediction and the reason can be inferred from the activation of fusion gates.


al (2013) incorporate a dedicated causal component into their system, and note that it improves the overall performance.

their model is limited by the need for lexical overlap between a causal construction found in their knowledge base and the question itself.


due to the directionality inherent in causality, they do not apply to our causal model so there we omit them.


In their approach, types are included as a part of unary lexicon for building the logical forms from natural language questions.

no explicit type inference is exploited.


The final vec-tor for this word, < 0.37, 0.18, 0.0 > with TF-IDF or < 0.36, 0.06, 0.0 > with PPMI-IDF, is intended to guide the implicit model toward a contrastive relation, thus potentially helping in identifying the relation in example (1b).

the word "week" is more likely to be found in the arguments of temporal relations that can be triggered by before but also while, an ambiguity kept in our representation whereas approaches based on using explicit examples as new training data generally choose to annotate them using the most frequent sense associated with the connective, often limiting themselves to the less ambiguous ones (Marcu and Echihabi, 2002;Sporleder and Lascarides, 2008;Lan et al., 2013;Braud and Denis, 2014;.


Discourse connectives are words (e.g., but, since) or grammaticalized multi-word expressions (e.g., as soon as, on the other hand) that may trigger a discourse relation.

these forms can also appear without any discourse reading, such as because in: He can't sleep because of the deadline.


use of raw tokens (One-hot), a conclusion in line with the results reported in (Braud and Denis, 2015) for binary systems.

contrary to their findings, in multiclass, the best results are not obtained using the Brown clusters, but rather the dense, real valued representations (Embed.


We have described our neural attention framework and a content-based model in previous subsection.

the model mentioned above ignores the location information between context word and aspect.


This case shows the effects of multiple hops.

in Table 4(b), the content-based model also gives a larger weight to "dreadful" when the target we focus on is "food".


Past research has proposed many techniques to extract opinion targets (we will just call them targets hereafter for simplicity) and also to classify sentiment polarities on the targets.

a target can be an entity or an aspect (part or attribute) of an entity.


Basically, they used topics generated from past domains to help current domain model inference.


We can regard the extracted terms from a NER system as entities and the rest of the targets as aspects.

a NER system cannot identify entities such as "this car" from "this car is great."


But our type modifier (TM) does that, i.e., if an opinion target appears after "this" or "these" in at least two sentences, TM labels the target as an entity; otherwise an aspect.


(2007) first chose pivot words which have high mutual information with the sentiment labels, and then set up the pivot prediction tasks to be the predictions of each of these pivot words using the other words.

the original SCL method is based on traditional discrete feature representations and linear classifiers.


They use Stacked Denoising Auto-encoders (SDA) to induce a hidden representation that presumably works well across domains.

sDA is fully unsupervised and does not consider the end task we need to solve, i.e., the sentiment classification task.


However, SDA is fully unsupervised and does not consider the end task we need to solve, i.e., the sentiment classification task.

the idea behind SCL is to use carefullychosen auxiliary tasks that correlate with the end task to induce a hidden representation.


However, these methods are still based on traditional discrete representation and do not exploit the idea of using auxiliary tasks that are related to the end task.

the sentence embeddings learned from our method are derived from real-valued feature vectors and rely on related auxiliary tasks.


Most of the state-of-the-art sentiment classification methods are based on supervised learning algorithms which require large amounts of manually labeled data.

the labeled resources are usually imbalanced in different languages.


There are two classes of mainstreaming sentiment classification algorithms: unsupervised methods which usually require a sentiment lexicon (Taboada et al., 2011) and supervised methods (Pang et al., 2002) which require manually labeled data.

both of these sentiment resources are unbalanced in different languages.


"I felt it could have been a lot better with a little less comedy and a little more drama to get the point across.

its still a must see for any Jim Carrey fan. "


As a general tendency, the performance of all approaches worsens as sentence length increases.

for sentences longer than 35 words we see that NMT quality degrades more markedly than in PBMT systems.


As for lexical errors, a number of existing taxonomies further distinguish among translation errors due to missing words, extra words, or incorrect lexical choice.

given the proven difficulty of disambiguating between these three subclasses (Popović and Ney, 2011;Fishel et al., 2012), we prefer to rely on a more coarse-grained linguistic error classification where lexical errors include all of them (Farrús Cabeceran et al., 2010).


than using it for source pre-ordering, as done by the HPB and SPB systems.

this only results in a moderate reduction of verb reordering errors (-12% and -25% vs. HPB and SPB respectively).


For instance, when the question is relatively simple and does not have complex compositional structure, paths in the knowledge graph that connect the answers and the entities in the narrative can be interpreted as legitimate semantic parses.

as we will show in our experiments, learning from implicit supervision alone is not a viable strategy for algebra word problems.


6 While their algorithms are interesting, the experimental setting is somewhat unrealistic as the implicit signals are simulated.

the goal of our algorithm is to significantly improve a strong solver with a large quantity of unlabeled data.


These practical issues make it difficult to use the crawled equations for explicit supervision directly.

it is relatively easy to obtain question-solution pairs with simple heuristics.


Recent advances in deep learning techniques demonstrate that the error rate of machine learning models can decrease dramatically when large quantities of labeled data are presented (Krizhevsky et al., 2012).

labeling natural language data has been shown to be expensive, and it has become one of the major bottleneck for advancing natural language understanding techniques (Clarke et al., 2010).


Social media especially contains time-sensitive information and requires accurate temporal analysis, for example, for detecting real-time cybersecurity events (Ritter et al., 2015;Chang et al., 2016), disease outbreaks (Kanhabua et al., 2012) and extracting personal information (Schwartz et al., 2015).

most work on social media simply uses generic temporal resolvers and therefore suffers from suboptimal performance.


(2016) also apply the similar idea of combining pointer networks and softmax output.

different from all these discriminative models above, we explore generative models for sentence compression.


For the related tasks of document and multidocument summarization, Graham (2015) provides a fine-grained comparison of automated evaluation methods.

to the best of our knowledge, no studies of automatic evaluation metrics exist for abstractive compression of shorter texts.


In particular, operations concerning deletion and verbal features are the most frequent ones both at lower and higher readability scores differences.

they have an opposite distribution: transformations of verbal features increase at higher readability differences (>0.6) while deletions decrease.


Nevertheless, those models built for single-turn response selection ignore the whole context information, which makes it difficult to be implemented in the multi-turn response selection tasks.

research on multi-turn response selection usually takes the whole context into consideration and views the context and response as word sequences.


The latent variable enables us to jointly model discourse arguments and their relations, rather than conditionally model y on x.

the incorporation of the latent variable makes the modeling difficult due to the intractable computation with respect to the posterior distribution.


With the parameters of Gaussian distribution, we can access the representation z using different sampling strategies.

traditional sampling approaches often breaks off the connection between recognizer and approximator, making the optimization difficult.


However, our work differs from theirs significantly, which can be summarized in the following three aspects: 1) they employ the recurrent neural network to represent the discourse arguments, while we use the simple feedforward neural network; 2) they treat the discourse relations directly as latent variables, rather than the underlying semantic representation of discourses; 3) their model is optimized in terms of the data likelihood, since the discourse relations are observed during training.

varNDRR is optimized under the variational theory.


When trained on NW and tested on DF, supervised methods encounter out-of-domain situations.


(2015) proposed a super Taxonomies which serve as the backbone of structured knowledge are useful for many NLP applications such as question answering (Harabagiu et al., 2003) and document clustering (Fodeh et al., 2011).

the hand-crafted, well-structured taxonomies including WordNet (Miller, 1995), Open-Cyc (Matuszek et al., 2006) and Freebase (Bollacker et al., 2008) that are publicly available may not be complete for new or specialized domains.


vised method to learn term embeddings based on pre-extracted taxonomic relation data.

this method is heavily dependent on the training data to discover all taxonomic relations, i.e.


Many works in the literature (Kozareva and Hovy, 2010;Navigli et al., 2011;Wentao et al., 2012) attempted to manually find these contextual patterns, or automatically learn them.

due to the wide range of complex linguistic structures, it is difficult to discover all possible contextual patterns between hypernyms and hyponyms in order to detect taxonomic relations effectively.


For example, in the Animal domain, our approach identifies 'wild sheep' as a hyponym of 'sheep', but in WordNet, they are siblings.

many references 1, 2 consider 'wild sheep' as a species of 'sheep'.


A method for inducing (binary) relations and the categories they connect was proposed by (Mohamed et al., 2011).

in that work, categories and their instances were known a-priori.


However, in that work, categories and their instances were known a-priori.

in case of SICTF, both categories and relations are to be induced.


we iteratively sample the partial changes χ k (i,j) .

we use equations 9 and 10 for predictive distributions of Dirichlet multinomials instead of 5 and 6.


In role-by-role probabilities (role conditioned on its parent role) we did not distinguish whether the role is on the left side or the right side of the parent.

this position keeps important information about the syntax of words (and their roles).


Modeling textual or visual information with vector representations trained from large language or visual datasets has been successfully explored in recent years.

tasks such as visual question answering require combining these vector representations with each other.


We compensated for this by stacking fully connected layers (with 4096 units per layer, ReLU activation, and dropout) after the non-bilinear pooling methods to increase their number of parameters.

even with similar parameter budgets, nonbilinear methods could not achieve the same accuracy as the MCB method.


CSP does not directly exploit this observation as it only updates its parameter vector with respect to the differences between complete assignments: w = w + ∆φ(x, y, z).

sWVP can exploit this observation in various ways.


Our approach is more amenable to such theoretical models than traditional distributed word embeddings.

we can also work with the expected word embeddings, which are vectors of probabilities, and can therefore be expected to hold the advantages of dense distributed representations (Bengio et al., 2013).


The original grammar performs slightly worse than the previous work in dependency tree-based translation, this can ascribed to the difference between the implementation of the original grammar and the dependency parser used in the previous work.

the similarized grammar achieves very significant improvement based on the original grammar, and also significant surpass the previous work.


Additionally, their model maps task difficulty and review bias to a difficulty parameter in IRT.

we naturally extended the model so that standard analysis approaches can be applied to maintain interpretability.


The model described above is different from the original GRM, which assumed that the values of a are independent from question to question, and that each a belongs to exactly one question.

in our problem setting, the judges evaluate multiple segments, and discrimination parameter a is independent from segment j.


Consequently, systems with low θ tended to lose to the baseline on segment 1858 due to their wrong translation (see the translation of "hawaiano de Mauna Kea").

some of the low-ranked systems beat the baseline on segment 1818, and the segment contributed to discriminate them.


In contrast, translations generated by neural models (both GroundHog and VNMT) are much more fluent and comprehensible.

there are essential differences between GroundHog and our VNMT.


The translation of the clause "体现 了 双方 可贵 的 和 平 诚意 。" at the end of the source sentence is completely lost.

our VNMT model does not miss or mistake these fragments and can convey the meaning of entire source sentence to the target side.


Specifically, for the model in question the EM algorithm still applies but we have to use a gradient-based algorithm within the learning step.

since such a gradient-based method introduces the necessary complication of a learning rate, we could also optimize the objective by picking θ and λ via cross-validation and using a multinomial EM algorithm for the learning of the lexical t terms.


For example, suppose word w 0 has k senses and thus it has k clusters of context windows, we denote the average embedding vectors for these clusters asξ 1 , • • • ,ξ k .

since the online dictionary uses some descriptions and example sentences to interpret each word sense, we can represent each word sense by the average embedding of those words including its description words and the words in the corresponding example sentences.


We could compare different models using the same number of total parameters.

this would inevitably introduce other biases, e.g., the number of hyper-parameters would become different.


2016have manually annotated these questions with logical forms.

automatic characterization of questions is hard, while manual characterization is costly and requires expertise.


(2016) show that only 66% of the WEBQUESTIONS answers, which were collected via crowdsourcing, are completely correct.

although questions generated from a KB may not follow the distribution of user information needs, it has the advantage of explicit question characteristics, and enables programmatic configuration of question generation.


PARASEMPRE, in a reverse manner, enumerates a set of logical forms, generates a canonical utterance for each logical form, and ranks logical forms according to how well the canonical utterance paraphrases the input question.

jA-CANA follows the information extraction paradigm, and builds a classifier to directly predict whether an individual is the answer.


The following experiments will give more insights about where the performance difference comes from.

jACANA is much faster, showing an advantage of information extraction.


If data is sparse, the posterior will still be sharply peaked around distributions in the gamma family, reducing the effective capacity of the model and minimizing overfitting.

given enough evidence in the data, the model will also deviate from the gamma-centered prior-depending on the kernel function chosen for the GP prior, any density function can in principle be represented.


Clearly, this includes all observations of saccade amplitudes and fixation durations observed in the training and test set.

we also need to evaluate the normalizer in Equation 26, and (for f ∈ {α 1 ,ᾱ 1 , α 2 , α 3 , α 4 }) the additional normalizer required when truncating the distribution (see Equation 15).


It is well known that there is substantial sociolinguistic variation between different communities, whether these communities are defined geographically (Trudgill, 1974) or via underlying sociocultural differences (Labov, 2006).

no previous work has systematically investigated community-specific variation in word sentiment at a large scale.


Of course, the sentiment lexicons induced by SENTPROP are not perfect, which is reflected in the uncertainty associated with our bootstrap-sampled estimates.

we believe that these userconstructed, domain-specific lexicons, which quantify uncertainty, provide a more principled foundation for CSS research compared to domain-general sentiment lexicons that contain unknown biases.


Most of these studies focus on building sentiment classifiers with features, which include bag-of-words and sentiment lexicons, using SVM (Mullen and Collier, 2004).

the results highly depend on the quality of features.


By utilizing syntax structures of sentences, tree-based LSTMs have been proved to be quite effective for many NLP tasks.

such methods may suffer from syntax parsing errors which are common in resourcelacking languages.


3 Attention-based LSTM with Aspect Embedding 3.1 Long Short-term Memory (LSTM) Recurrent Neural Network(RNN) is an extension of conventional feed-forward neural network.

standard RNN has the gradient vanishing or exploding problems.


(2015) proposed to combine deep learning and structured learning for language parsing which can be learned by structured perceptron.

they also separate neural network training with structured prediction.


Most existing works focus on either generic subjective expression or aspect expression extraction.

in opinion mining, it is often desirable to mine the aspect specific opinion expressions (or aspectsentiment phrases) containing both the aspect and the opinion.


In Yang and Cardie, (2012) a semi-CRF based approach is used which allow sequence labeling at segment level and (Yang and Cardie, 2014) employed semi-CRF for opinion expression intensity and polarity classification.

all the above works focus on generic subjective expressions as opposed to aspect specific opinionsentiment phrases.


It is a bit unfair to compare PSM with sMC-GPU because PSM is lacking phrase rank optimization whereas sMC-GPU enforces it, and the p@n metric uses rank position as its goodness criterion.

we will see that in an actual application task, both PSM and PSM-GPU does better than sMC-GPU.


We note that the Topic Coherence (TC) metric in (Mimno et al., 2011) which is often used to approximate coherence in unigram topic models as it correlates with human notions of coherence, uses codocument frequency of individual words in topics.

in our problem as phrases are sparse, their co-document frequency is far lower than words.


Automatically identifying users' emotional states from their texts and classifying emotions into finite categories such as joy, anger, disgust, etc., can be considered as a text classification problem.

it introduces a challenging learning scenario where multiple emotions with different intensities are often found in a single sentence.


In both models an annotator's response depends on an item only through its correct label.

iRT assumes a more sophisticated response mechanism involving both annotator qualities and item characteristics.


Then, it can be seen that if we sample uniformly from (w, c) ∈ Ω and c ∈ C \ {c}, then j(w, c, c which does not contain any expensive summations and is an unbiased estimator of (13), i.e., E [j(w, c, c )] = J (U, V, Ξ).

one can optimize ξ w,c exactly by using (12).


The update (12) indicates that ξ −1 w,c is proportional to rank (w, c).

one can observe that the loss function (•) in 14is weighted by a ρ ξ −1 w,c term.


Besides these state-of-the-art techniques, a few ranking-based approaches have been proposed for word embedding recently, e.g., (Collobert and Weston, 2008;Vilnis and McCallum, 2015;Liu et al., 2015).

all of them adopt a pair-wise binary classification approach with a linear ranking loss ρ 0 .


On one hand, semantic patterns may exist in neural representations that have not been fully captured by state-of-the-art word embeddings derived from text corpora.

we can see that the variation of the correlation coefficients for the WordSim-353 dataset with different β values is small.


(2015) proposed an improved approach to the concept identification subtask by using a simple classifier over actions which generate these subgraphs.

the overall architecture is still based on the pipelined model.


The underlying learning algorithm has shown the effectiveness on some other Natural Language Processing (NLP) tasks, such as dependency parsing and extraction of entity mentions and relations (Collins and Roark, 2004;Hatori et al., 2012;Li and Ji, 2014).

compared to these NLP tasks, the AMR parsing is more challenging in that the AMR graph is more complicated.


Comprehenders do not wait until the end of the sentence before they build a syntactic or semantic representation for the sentence.

the challenges of successfully applying the incremental joint model to this problem formulation are: 1) how can we design an effective decoding algorithm for identifying the relations between the nodes in an incremental fashion, given a partial sequence of spans, i.e., a partial sequence of goldstandard concept fragments; 2) further, if given a sentence, how can we design an incremental framework to perform concept identification and relation identification simultaneously.